Data analysis is a crucial skill that enhances your decision-making abilities. It acts as a significant driver in both your professional and personal life. Whether you're managing personal finances or evaluating customer feedback, data analysis is key to progressing in your career.

If you're aiming to improve your skills at work or embark on a career in data analytics, this article is tailored for you.

TECHNIQUES.

Issue Identification;

first step in the process is to identify the specific issue you intend to solve.

Data Collection;

gathering information that is relevant to the issue. Data can be considered in two main forms Primary data, collection of raw information or Secondary data is more of recorded information.

Data Cleaning;

essentially entails removing of unwanted information in the gathered data, ensuring information being analyzed and issued is relevant and correct.

Data Analysis;

mainly characterized into four, Descriptive involves taking apart data and summarizing its main attributes. Diagnostic focuses on why something has happened. Predictive enables you to identify trends based on historical data. Prescriptive aims to determine your research’s best course of action.

Data Interpretation;

visualizing findings of the data to present insights in an understandable ways.

TOOLS.

Data in hand, what comes next?

Microsoft Excel;

ideal for non-techies to perform basic data analysis and create charts and reports.

Google Sheets;

known for its seamless collaboration capabilities including sorting, filtering, and simple calculations.

Rapid Miner;

ideal for data mining and model development, offers machine learning and predictive analytics capabilities.

Tableau;

for responsive dashboards, visually appealing and interactive data representations.

Power BI;

incredible data integration features and interactive reporting.

To build a successful career as a data scientist, there are a few things to consider. From understanding the different roles available within the industry and realizing what technical skills you’ll need, there’s a lot to unpack as an early-stage data practitioner.